• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.124-131
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• 2009

A reliable detection of regions in radiography is one of the most important task before the evaluation of defects on welded joints. The extracted features is to be classified into distinctive clusters for each segmented region. But conventional segmentation techniques give unsatisfactory results for this task due to the spatial superposition of intensity and low signal-to-ratio(SNR) in radiographic images. The usage of global or local processes not only provide the necessary noise resistance but also fail in classification of regions. In this paper, we presents an appropriate approach for segmentation of region-based indications in industrial Computed Radiography(CR) images. The geometric differences between welded and non-welded area which is generated on radiography as the representative regions(background, thickness, middle and welded region in steel tube image) have constructed the hierarchical structure. Although this structure is contaminated by noise, the scheme between regions can be selected by the help of local clustering based on distinctive geometric property of each region. Because of the geometric nature of the considered region and so that the region is selected layer by layer, and that the real class represents the boundary between regions, the vertical and horizontal clustering process in each layer must be judicious. In order to show the effectiveness of this approach, a comparative experiment of various segmentation method is performed on industrial steel tube CR images.

• Investigative Magnetic Resonance Imaging
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• v.9 no.1
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• pp.2-8
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• 2005

Purpose : The purpose of this study was to implement a software to visualize tumor and its surrounding fiber tracts simultaneously using diffusion tensor imaging and examine the feasibility of our software for investigating the influence of tumor on its surrounding fiber connectivity. Material and Methods : MR examination including T1-weigted and diffusion tensor images of a patient with brain tumor was performed on a 3.0 T MRI unit. We used the skull-striped brain and segmented tumor images for volume/surface rendering and anatomical information from contrast-enhanced T1-weighted images. Diffusion tensor images for the white matter fiber-tractography were acquired using a SE-EPI with a diffusion scheme of 25 directions. Fiber-tractography was performed using the streamline and tensorline methods. To correct a spatial mismatch between T1-weighted and diffusion tensor images, they were coregistered using a SPM. Our software was implemented under window-based PC system. Results : We successfully implemented the integrated visualization of the fiber tracts with tube-like surfaces, cortical surface and the tumor with volume/surface renderings in a patient with brain tumor. Conclusion : Our result showed the feasibility of the integrated visualization of brain tumor and its surrounding fiber tracts. In addition, our implementation for integrated visualization can be utilized to navigate the brain for the quantitative analysis of fractional anisotropy to assess changes in the white matter tract integrity of edematic and peri-edematic regions in a number of tumor patients.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.6
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• pp.469-477
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• 2008

Purpose: The goal of this paper is to present the design and performance of a position encoding circuit for $16{\times}16$ array of position sensitive multi-anode photomultiplier tube for small animal PET scanners. This circuit which reduces the number of readout channels from 256 to 4 channels is based on a charge division method utilizing a resistor array. Materials and Methods: The position encoding circuit was simulated with PSpice before fabrication. The position encoding circuit reads out the signals from H9500 flat panel PMTs (Hamamatsu Photonics K.K., Japan) on which $1.5{\times}1.5{\times}7.0\;mm^3$ $L_{0.9}GSO$ ($Lu_{1.8}Gd_{0.2}SiO_{5}:Ce$) crystals were mounted. For coincidence detection, two different PET modules were used. One PET module consisted of a $29{\times}29\;L_{0.9}GSO$ crystal layer, and the other PET module two $28{\times}28$ and $29{\times}29\;L_{0.9}GSO$ crystal layers which have relative offsets by half a crystal pitch in x- and y-directions. The crystal mapping algorithm was also developed to identify crystals. Results: Each crystal was clearly visible in flood images. The crystal identification capability was enhanced further by changing the values of resistors near the edge of the resistor array. Energy resolutions of individual crystal were about 11.6%(SD 1.6). The flood images were segmented well with the proposed crystal mapping algorithm. Conclusion: The position encoding circuit resulted in a clear separation of crystals and sufficient energy resolutions with H9500 flat-panel PMT and $L_{0.9}GSO$ crystals. This circuit is good enough for use in small animal PET scanners.